Web opinion mining

Harb, Ali; Plantié, Michel; Dray, Gérard; Roche, Mathieu; Trousset, François; Poncelet, Pascal

doi:10.1145/1456223.1456269

Cited by 65 publications

(11 citation statements)

References 9 publications

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“…In literature, many researchers used IMDB dataset with 2 classes for performance testing of proposed algorithms. Results of different techniques for text classification in IMDB dataset are available in [3,5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. IMDB dataset for web opinion mining is investigated in [5].…”

Section: Related Workmentioning

confidence: 99%

“…Application field like life science, social media, business intelligence, healthcare etc needs text mining [2]. The purpose of text mining is multi-fold namely feature selection [1,3], feature extraction [4], web opinion mining [3,5] etc. Text classification applications are present in various domains namely social media [6,7], healthcare industries [8], business intelligence, image processing etc.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al reported the significance of five techniques for text categorization [12]. Techniques namely support vector machine [13], KNN [14], association-based classification [5], term gram model [15], Bayesian classification [16,17], and neural network [18] used for text classification. Each classification technique has some limitations.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Adamuthe¹,

Jagtap²

2019

IJISA

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This paper presents an investigation of the convolutional neural network (CNN) with Word2Vec word embedding technique for text classification. Performance of CNN is tested on seven benchmark datasets with a different number of classes, training and testing samples. Test classification results obtained from proposed CNN are compared with results of CNN models and other classifiers reported in the literature. Investigation shows that CNN models are better suitable for text classification than other techniques. The main objective of the paper is to identify best-fitted parameter values batch size, epochs, activation function, dropout rates and feature maps values. Results of proposed CNN are better than many other classification techniques reported in the literature for Yelp Review Polarity dataset and Amazon Review Polarity dataset. For all the seven datasets, accuracy obtained by proposed CNN is close to the best-known results from the literature.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Adamuthe¹,

Jagtap²

2019

IJISA

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“…This corpus is needed to learn and classify descriptors denoting a positive or negative opinion. Opinion descriptors are specific to the thematic in which there are used ( [5]) and their interpretation may drastically change from one thematic or context to another. For example, let us consider the two following sentences: "The picture quality of this camera is high" and "The ceilings of the building are high".…”

Section: Acquiring the Training Corpus Automaticallymentioning

confidence: 99%

“…The way opinions are expressed may be quite different from one document to another and are often specific to the thematic the document deals with. Thus the vocabulary which is used depends on this thematics ( [5]). This vocabulary is then automatically learned for the thematic prior to any text or opinion extraction.…”

Section: Introductionmentioning

confidence: 99%

Opinion Extraction Applied to Criteria

Duthil¹,

Trousset²,

Dray³

et al. 2012

Lecture Notes in Computer Science

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Abstract. The success of Information technologies and associated services (e.g., blogs, forums,...) eases the way to express massive opinion on various topics. Recently new techniques known as opinion mining have emerged. One of their main goals is to automatically extract a global trend from expressed opinions. While it is easy to get this overall assessment, a more detailed analysis will highlight that the opinions are expressed on more specific topics: one will acclaim a movie for its soundtrack and another will criticize it for its scenario. Opinion mining approaches have little explored this multicriteria aspect. In this paper we propose an automatic extraction of text segments related to a set of criteria. The opinion expressed in each text segment is then automatically extracted. From a small set of opinion keywords, our approach automatically builds a training set of texts from the web. A lexicon reflecting the polarity of words is then extracted from this training corpus. This lexicon is then used to compute the polarity of extracted text segments. Experiments show the efficiency of our approach.

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An Ontology‐Based Contextual Data Modeling for Process Improvement in Healthcare

Abirami¹,

Askarunisa²

2021

Semantic Web for Effective Healthcare

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Web opinion mining

Cited by 65 publications

References 9 publications

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

Opinion Extraction Applied to Criteria

An Ontology‐Based Contextual Data Modeling for Process Improvement in Healthcare

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